Data Engineering • Interactive Module

Module 14: Data Quality, Testing, Metadata and Lineage

Master enterprise data quality engineering: dimensions of quality, data profiling, rule-based validation, schema registries, referential integrity, automated testing (dbt, Great Expectations), anomaly detection, metadata architecture (DataHub, OpenMetadata), data contracts, and column-level impact lineage.

Concept Overview & Direct Technical Answer

Direct Definition: Master enterprise data quality engineering: dimensions of quality, data profiling, rule-based validation, schema registries, referential integrity, automated testing (dbt, Great Expectations), anomaly detection, metadata architecture (DataHub, OpenMetadata), data contracts, and column-level impact lineage.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/data-engineering/data-quality-lineage. Free, open curriculum for software engineers, systems practitioners, and interview candidates.